Radar targets with moving components (engines, propellers, etc.) are well characterized in the frequency domain so that the estimation of spectral parameters can be used in the process of extracting features for their classification. With non-imaging scanning radars the application of spectral parameter estimation for target classification is limited by a short time on target and other radar parameters. To alleviate these limitations, artificial neural networks have been used bringing with them the long training time issue. This paper suggests a neural network approach that capitalizes on the prescence of spectral components and reduces the training time. This approach divides the training of the multilayer perceptron (MLP) in two steps: pretraining and final training. Pretraining guides the MLP to recognize the Fourier basis set of signals. The final training for target classification is then facilitated.

The application of adaptive arrays to communications is addressed for the specific case of known pulsed desired signal transition times. From this information, the adaptive antenna weights that maximize gain on the desired signal and null out the interferers are described. The approach is then extended to cases when the interferer(s) power is not stationary, i.e., this quantity varies versus time.>

Two adaptive algorithms are presented for the detection of small changes in a pair of images in a low signal to clutter plus noise ratio (SCNR) environment. They both have the ability to track the nonstationary image signals and suppress the clutter plus noise background. Both detectors are based on the adaptive correlation canceling technique. One algorithm uses an order recursive least squares (ORLS) lattice filter, while the other is based on the two-dimensional least mean square (TDLMS) algorithm. The only a priori information required by the algorithms is that the background clutter plus noise in the pair of images is spatially correlated. An analytical expression for the improvement factor for the change detectors is presented. The performance of the two algorithms is evaluated by using an optical satellite image, with computer generated target and noise added.>

A recursive least-squares adaptive lattice algorithm is applied to equalizing a communication channel carrying time division multiplexed (TDM) signals. The procedure utilizes one lattice predictor subsystem for all TDM signals before demultiplexing. The total TDM signal serves as a training sequence, and the minimization is performed on the sum of the squares of the total joint error residual. The weighted backward error output of the lattice predictor is then multiplexed, and the estimate of each signal is obtained. This way, a significant savings in hardware can be obtained. Computer simulation is run for both multiplexed signals. The results show that the equalizer performance is the same as in the case of a single transmitted signal.>

In this paper we treat the problem of joint Doppler and elevation estimation for low-elevation moving target in the antenna array's near field. In general, the target can be embedded in a strong stationary clutter, and the dwell time on target is such that the available number of snapshots, K, can be rather small (twelve to sixteen). Our approach is to first locate the Doppler shift, suppress the stationary clutter, and coherently demodulate the signal, all in frequency domain. The demodulators' outputs are converted back to time-domain and, since their Doppler shifts are now equal to zero, the narrowband version of superresolution angle estimation methods can be applied. That is all of the K snapshots remain available for spatial processing.

The high resolution vector space methods of angle-of-arrival estimation which are based on the eigendecomposition of the covariance matrix of the received signal are known to break down when the incoming signals are fully correlated. In order to alleviate this problem, the covariance matrix is preprocessed using spatial smoothing averaging. An alternative method which is based on Singular Value Decomposition of data matrix is capable of resolving coherent plane waves with no need for any preprocessing. In this paper we have studied the characteristics of the MUSIC and the SVD-based Minimum Norm methods in terms of their bias and resolution in the coherent signal environment. Using Monte Carlo simulations we have shown that the Minimum Norm method has better capability in resolving coherent signals than the MUSIC algorithm, provided that the variations in beam pattern due to the inter-signal phase can be tolerated.

In this paper we present the results of the research on radar target elevation estimation in the near field of the vertical line array antenna. In particular, we examine the case of targets at low elevations where coherent reflections from ground prevent accurate measurements of target angular positions. The estimation method used is a modified version of the MUSIC algorithm, adapted for the application in the near field antenna environment. A spatial smoothing preprocessing scheme has been applied in order to resolve close coherent signals. The algorithm has been successfully applied go both simulated and recorded radar data. Controlled Ku-band radar experiments were conducted at the Valley Forge Research Center of the University of Pennsylvania. Estimation of actual target elevations to within 1 standard beamwidth has been achieved for SNR's of about 0 db.

In this paper we investigate the resolution properties of two linear prediction algorithms for bearing estimation with line array of sensors, in the presence of either incoherent or coherent sources: the Burg algorithm and the generalized Burg algorithm. It was demonstrated by computer simulation that both algorithms achieve the superresolution of incoherent sources. For coherent sources, however, it was shown that the Burg estimates are sensitive to the magnitude and the phase of the inter-signal correlation, while the generalized Burg estimates are comparable to those obtained for the incoherent sources and are less sensitive to intersignal correlation.

In the case of nonstationary signal fields, e.g., spatial array beamforming in the presence of correlated multipath, an autoregressive angle estimator should employ the generalized Burg algorithm (GBA) to account for the non-Toeplitz structure of the power cross-spectral density matrix. The detection performance of the GBA is analyzed and is shown to be comparable to the performance of the conventional AR detector in the case where no correlated interference is present.

Array beamforming employing nonlinear methods of spectral estimation presents a difficult problem when the spatial field is inhomogeneous, e.g., when correlated multipath is present, and hence, the corresponding cross-spectral (CS) matrix is non-Toeplitz. To overcome this difficulty in the maximum entropy (ME) beamforming, we present a generalization of the Burg algorithm for dealing with a non-Toeplitz CS matrix. The stability and minimum phase properties of the original Burg algorithm are retained. The use of the generalized Burg algorithm for elevation angle estimation is illustrated for the case of a passive sonar field. Statistical properties of the estimator are analyzed using (1) uncorrelated Gaussian noise only, and (2) the direct and specular multipath components of the target signal, embedded in uncorrelated noise and in directional ambient noise.

A generalization of Burg's algorithm is described for the maximum-entropy (ME) field-mapping of a space-time series that is characterized by a non-Toeplitz cross-spectral (CS) matrix. Stability of the original Burg algorithm is retained, that is, the reflection coefficient is less then unity in magnitude at each recursion of the algorithm. Examples are included to illustrate applicability of the generalized algorithm to spatial mapping of correlated sources, as encountered in an under-water acoustic environment.

An adaptive digital filtering scheme is presented which uses a lattice structure for adaptive prediction and elimination of radar clutter, using Burg's algorithm for the computation of the lattice coefficients. This method computes a minimum-phase prediction error filter directly from the radar data, while not having the end-bias problems common to many filtering schemes. This permits quick adaptation to changing clutter conditions. An integration decay constant allows the filter to adapt to longer duration signals (clutter) while passing shorter duration signals (targets). The filter design is described and experimental results are discussed.

A brief review of the maximum entropy method for spectral analysis of complex signals is presented. It is shown that, for the spectral analysis of radar clutter, reliable short-term spectral estimates can be obtained with a small number of data points, while keeping a good resolution capability.